## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.162 -2.412 -1.213 1.429 13.075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.241e+00 1.714e-01 30.58 < 2e-16 ***
## CFU -1.455e-08 2.479e-09 -5.87 8.24e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.272 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.06859, Adjusted R-squared: 0.0666
## F-statistic: 34.46 on 1 and 468 DF, p-value: 8.244e-09
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.753 -15.274 -4.381 12.851 72.405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.609e+01 1.047e+00 44.03 < 2e-16 ***
## CFU -1.104e-07 1.514e-08 -7.29 1.33e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.99 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.102, Adjusted R-squared: 0.1001
## F-statistic: 53.14 on 1 and 468 DF, p-value: 1.331e-12
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.65813 -0.51173 -0.06297 0.45801 2.09371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.899e+00 3.404e-02 55.801 < 2e-16 ***
## CFU -3.098e-09 4.923e-10 -6.293 7.15e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6498 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.07803, Adjusted R-squared: 0.07606
## F-statistic: 39.61 on 1 and 468 DF, p-value: 7.149e-10
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9720 -1.5949 -0.8509 0.8313 14.2621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.051e+00 1.503e-01 26.944 < 2e-16 ***
## CFU -6.395e-09 1.924e-09 -3.324 0.000978 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.435 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.0293, Adjusted R-squared: 0.02665
## F-statistic: 11.05 on 1 and 366 DF, p-value: 0.0009777
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.215 -12.009 -2.285 9.603 58.318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.882e+01 9.625e-01 40.329 < 2e-16 ***
## CFU -6.051e-08 1.232e-08 -4.913 1.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.59 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.06187, Adjusted R-squared: 0.0593
## F-statistic: 24.14 on 1 and 366 DF, p-value: 1.356e-06
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.43246 -0.41705 -0.08106 0.43371 1.47089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.674e+00 3.495e-02 47.885 < 2e-16 ***
## CFU -1.551e-09 4.473e-10 -3.468 0.000588 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5662 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.03181, Adjusted R-squared: 0.02916
## F-statistic: 12.02 on 1 and 366 DF, p-value: 0.0005875
After talking with Pat (1/17/18) Does end point look like initial? are most similar the recovered ones? Split analysis by abx/dose/days recovered Resistance more similar than colonized? Start with high dose and 1 day recovery then look at how modulating the dose/recovery affects train model with low recovery and test with high recovery Show different context of day 0 compare differences in metro recovery how do susceptibility break points compare?
the metadata file has the following columns group CFU - ranges 0 to 8.1e8 with 601 NAs (most of NAs are on days when cdiff was not present, so can change to 0 expect for NAs after day 1) cage mouse day - ranges -11 to 10 abx - amp cef cipro clinda metro none strep vanc 405 379 83 190 339 3 362 312 dose - 0.1 0.3 0.5 0.625 1 10mg/kg 5 NA’s 304 253 653 112 339 273 136 3 dose abx cages mice
10 cipro
10 clinda
0.1 cef
0.3 cef 0.5 cef 1 metro 0.1 strep 0.5 strep 5 strep 0.1 vanc 0.3 vanc 0.625 vanc cdiff - if sample was treated challenged with C. difficile logical T(1770), F(303) delayed - if sample was allowed extra days to recover from abx treatment logical T(455), F(1618) preAbx - if sample collected prior to abx treatment logical T(154), F(1919) recovDays - how many days after stopping abx (metro and amp for 5 day recovery) range 1 to 5
only one mouse not given abx but is listed as preAbx F for -5 possible to denote mock abx treatment(?) question about mouse 600-2D-6 (cef - delivered via water) should be pre-antibiotic but is listed as F all other mice in cage are preAbx on day -6, except this one since this abx was delivered via drinking water, it is likely clerical error, need to write a check script to make sure all mice in each cage all are recorded to have the same treatment
## # A tibble: 100 x 6
## otu median_abundance rho pvalue pvalue_BH pvalue_bon
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Otu000003 1.26 -0.614 4.17e-50 7.76e-48 7.76e-48
## 2 Otu000064 0. -0.606 1.52e-48 1.41e-46 2.83e-46
## 3 Otu000070 0. -0.531 1.40e-35 6.88e-34 2.61e-33
## 4 Otu000006 0. -0.531 1.48e-35 6.88e-34 2.75e-33
## 5 Otu000041 0. -0.484 5.64e-29 2.10e-27 1.05e-26
## 6 Otu000010 1.65 0.482 1.05e-28 3.26e-27 1.96e-26
## 7 Otu000017 0.100 -0.470 3.47e-27 8.35e-26 6.46e-25
## 8 Otu000057 0. -0.470 3.59e-27 8.35e-26 6.68e-25
## 9 Otu000031 0. -0.459 6.39e-26 1.32e-24 1.19e-23
## 10 Otu000050 0. -0.453 3.83e-25 7.11e-24 7.11e-23
## # ... with 90 more rows